@Machine_learn
Course Curriculm #course
#ML
1. Welcome to the Applied Machine Learning Course
2. Introduction to Data Science and Machine Learning
3. Introduction to the Course
4. Setting up your system
5. Python for Data Science
6. Statistics For Data Science
7. Basics Steps of Machine Learning and EDA
8. Data Manipulation and Visualization
9. Project: EDA - Customer Churn Analysis
10. Share your Learnings
11. Build Your First Predictive Model
12. Evaluation Metrics
13. Build Your First ML Model: k-NN
14. Selecting the Right Model
15. Linear Models
16. Project: Customer Churn Prediction
17. Dimensionality Reduction (Part I)
18. Decision Tree
19. Feature Engineering
20. Share your Learnings
21. Project: NYC Taxi Trip Duration prediction
22. Working with Text Data
23. Naïve Bayes
24. Multiclass and Multilabel
25. Project: Web Page Classification
26. Basics of Ensemble Techniques
27. Advance Ensemble Techniques
28. Project: Ensemble Model on NYC Taxi Trip Duration Prediction
29. Share your Learnings
30. Advance Dimensionality Reduction
31. Support Vector Machine
32. Unsupervised Machine Learning Methods
33 AutoML and Dask
34. Neural Network
35. Model Deployment
36. Interpretability of Machine Learning Models
.https://courses.analyticsvidhya.com/courses/applied-machine-learning-beginner-to-professional?utm_source=sendinblue&utm_campaign=July_Newsletter_2019&utm_medium=email
Course Curriculm #course
#ML
1. Welcome to the Applied Machine Learning Course
2. Introduction to Data Science and Machine Learning
3. Introduction to the Course
4. Setting up your system
5. Python for Data Science
6. Statistics For Data Science
7. Basics Steps of Machine Learning and EDA
8. Data Manipulation and Visualization
9. Project: EDA - Customer Churn Analysis
10. Share your Learnings
11. Build Your First Predictive Model
12. Evaluation Metrics
13. Build Your First ML Model: k-NN
14. Selecting the Right Model
15. Linear Models
16. Project: Customer Churn Prediction
17. Dimensionality Reduction (Part I)
18. Decision Tree
19. Feature Engineering
20. Share your Learnings
21. Project: NYC Taxi Trip Duration prediction
22. Working with Text Data
23. Naïve Bayes
24. Multiclass and Multilabel
25. Project: Web Page Classification
26. Basics of Ensemble Techniques
27. Advance Ensemble Techniques
28. Project: Ensemble Model on NYC Taxi Trip Duration Prediction
29. Share your Learnings
30. Advance Dimensionality Reduction
31. Support Vector Machine
32. Unsupervised Machine Learning Methods
33 AutoML and Dask
34. Neural Network
35. Model Deployment
36. Interpretability of Machine Learning Models
.https://courses.analyticsvidhya.com/courses/applied-machine-learning-beginner-to-professional?utm_source=sendinblue&utm_campaign=July_Newsletter_2019&utm_medium=email
🖇 @Machine_learn
Facebook is open-sourcing DLRM — a state-of-the-art deep learning recommendation model to help AI researchers and the systems and hardware community develop new, more efficient ways to work with categorical data.
fb: https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/
link: https://arxiv.org/abs/1906.03109
Facebook is open-sourcing DLRM — a state-of-the-art deep learning recommendation model to help AI researchers and the systems and hardware community develop new, more efficient ways to work with categorical data.
fb: https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/
link: https://arxiv.org/abs/1906.03109
Meta
We are open-sourcing a state-of-the-art deep learning recommendation model to help AI researchers and the systems and hardware…
Deep learning with Python Develop Deep Learning models on Theano and Thensorflow using Keras
#book #keras #DL
@Machine_learn
#book #keras #DL
@Machine_learn
5_6133943928459624650.pdf
5.4 MB
Deep learning with Python Develop Deep Learning models on Theano and Thensorflow using Keras
#book #keras #DL
@Machine_learn
#book #keras #DL
@Machine_learn
@Machine_learn
#NLP #DL #course
New fast.ai course: A Code-First Introduction to Natural Language Processing
https://www.fast.ai/2019/07/08/fastai-nlp/
Github: https://github.com/fastai/course-nlp
Videos: https://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQjuVxglSDYWsSh9
#NLP #DL #course
New fast.ai course: A Code-First Introduction to Natural Language Processing
https://www.fast.ai/2019/07/08/fastai-nlp/
Github: https://github.com/fastai/course-nlp
Videos: https://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQjuVxglSDYWsSh9
@Machine_learn
Deep Learning For Real Time Streaming Data With Kafka And Tensorflow
#ODSC #DeepLearning #Tensorflow
https://www.youtube.com/watch?v=HenBuC4ATb0
Deep Learning For Real Time Streaming Data With Kafka And Tensorflow
#ODSC #DeepLearning #Tensorflow
https://www.youtube.com/watch?v=HenBuC4ATb0
@Machine_learn
__________________________
How to Develop an Information Maximizing GAN (InfoGAN) in Keras
https://machinelearningmastery.com/how-to-develop-an-information-maximizing-generative-adversarial-network-infogan-in-keras/
__________________________
How to Develop an Information Maximizing GAN (InfoGAN) in Keras
https://machinelearningmastery.com/how-to-develop-an-information-maximizing-generative-adversarial-network-infogan-in-keras/
Simple Deep Learning for
Programmers Write your own modern neural networks in Keras and Python for images and sequence data
#By: The Lazy Programmer
#book #DL
@Machine_learn
Programmers Write your own modern neural networks in Keras and Python for images and sequence data
#By: The Lazy Programmer
#book #DL
@Machine_learn
4_5773660197402707477.pdf
1.8 MB
Simple Deep Learning for
Programmers Write your own modern neural networks in Keras and Python for images and sequence data
#By: The Lazy Programmer
#book #DL
@Machine_learn
Programmers Write your own modern neural networks in Keras and Python for images and sequence data
#By: The Lazy Programmer
#book #DL
@Machine_learn
Forwarded from Ramin Mousa
@Machine_learn #code #paper
FixRes is a simple method for fixing the train-test resolution discrepancy. It can improve the performance of any convolutional neural network architecture.
Github: https://github.com/facebookresearch/FixRes
Article:https://arxiv.org/abs/1906.06423
FixRes is a simple method for fixing the train-test resolution discrepancy. It can improve the performance of any convolutional neural network architecture.
Github: https://github.com/facebookresearch/FixRes
Article:https://arxiv.org/abs/1906.06423
Forwarded from Ramin Mousa
1906.06423.pdf
897.7 KB
@Machine_learn #code #paper
FixRes is a simple method for fixing the train-test resolution discrepancy. It can improve the performance of any convolutional neural network architecture.
Github: https://github.com/facebookresearch/FixRes
Article:https://arxiv.org/abs/1906.06423
FixRes is a simple method for fixing the train-test resolution discrepancy. It can improve the performance of any convolutional neural network architecture.
Github: https://github.com/facebookresearch/FixRes
Article:https://arxiv.org/abs/1906.06423
@Machine_learn
#code #paper
Y-Autoencoders: disentangling latent representations via sequential-encoding
Article: https://arxiv.org/abs/1907.10949
GitHub: https://github.com/mpatacchiola/Y-AE
#code #paper
Y-Autoencoders: disentangling latent representations via sequential-encoding
Article: https://arxiv.org/abs/1907.10949
GitHub: https://github.com/mpatacchiola/Y-AE
arXiv.org
Y-Autoencoders: disentangling latent representations via...
In the last few years there have been important advancements in generative models with the two dominant approaches being Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)....
@Machine_learn
#CapsuleNet #code
Stacked Capsule Autoencoders
http://akosiorek.github.io/ml/2019/06/23/stacked_capsule_autoencoders.htm
#CapsuleNet #code
Stacked Capsule Autoencoders
http://akosiorek.github.io/ml/2019/06/23/stacked_capsule_autoencoders.htm